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Vetted Large Language Models Professionals
Pre-screened and vetted.
Fatemeh Taghvaei
Junior AI/ML Engineer and Instructor specializing in deep learning, computer vision, and NLP
“Computer-vision practitioner and educator who built a real-time license plate recognition system (OpenCV/Python + KNN) optimized to run on a Raspberry Pi with camera integration. Also designs hands-on deep learning coursework, incorporating recent transformer-based vision research (Vision Transformers) into practical labs on real datasets.”
Bhanu Gummadi
Mid-level Backend Software Engineer specializing in cloud-native microservices and FinTech
“Backend-focused engineer with Mastercard experience building and operating high-volume transaction-processing microservices. Has owned customer-facing banking services end-to-end and built an internal on-call analytics tool that centralized logs/metrics with real-time filtering to speed root-cause analysis and reduce incident investigation time.”
Pree Simphliphan
Intern Software Engineer specializing in robotics, embedded systems, and AI
“Senior design robotics engineer on a "Grocery Robot" project selected for the final round of the $10K SICK Challenge, owning ROS2 system design and behavior-tree-based task orchestration across multiple independently developed modules. Also implemented I2C/ESP32 collision avoidance, IK control for a robotic arm, and a Node.js ordering system, with additional research experience using RPLIDAR-based SLAM.”
Gary Chang
Senior Full-Stack Software Engineer specializing in AI-first cloud-native systems
“End-to-end engineer who has productionized AI automation and RAG capabilities, building full-stack systems (React/Node/Redis/Postgres + vector DB) with evaluation-driven quality gates and monitoring. Reported ~60% reduction in manual ops time and major turnaround improvements, and has experience modernizing legacy systems safely via feature flags and parallel runs while working across product, data, and ops teams (System1).”
Lokesh Jain
Senior AI/ML Engineer specializing in supply chain and healthcare systems
“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”
Mid-level AI/ML Engineer specializing in NLP, MLOps, and production ML systems
Entry-level Machine Learning Engineer specializing in multimodal AI and LLM systems
Junior AI Software Engineer specializing in Android ML and LLM-powered recommendations
Senior Backend Engineer specializing in cloud-native JavaScript platforms and LLM integrations
Mid-level Data Scientist specializing in LLMs, fraud detection, and healthcare analytics
Senior AI/ML Engineer specializing in Generative AI agents and RAG systems
Junior NLP/ML Engineer specializing in LLM fine-tuning and long-context biomedical NLP
Mid-level AI/ML Engineer specializing in LLM chatbots and computer vision for medical imaging
Junior AI/ML Engineer specializing in NLP, LLMs, and production ML systems
Entry-level Machine Learning Engineer specializing in healthcare and analytics
Mid-level Full-Stack Software Engineer specializing in AI/ML and scalable web systems
Intern Machine Learning Engineer specializing in RAG, semantic search, and applied NLP
Mid-level Software Engineer specializing in full-stack, cloud, and AI systems
Mid-level Software Engineer specializing in Python, GenAI/ML, and cloud-native systems